China Agricultural University Researchers have exceeded the planed publications of the project, with many important research outputs generated.

They have published or submitted 11 papers and articles associated to research developed in the AquaDetector project, some of which are available online.

We welcome you to explore our research!

1. Haixia Li, Yu Guo, Huajian Zhao, Yang Wang, David Chow, (2021) Towards automated greenhouse: A state of the art review on greenhouse monitoring methods and technologies based on internet of things, Computers and Electronics in Agriculture, 191, Elsevier B.V.

2. Wang, G.; Muhammad, A.; Liu, C.; Du, L.; Li, D. Automatic Recognition of Fish Behavior with a Fusion of RGB and Optical Flow Data Based on Deep Learning. Animals. 2021, 11, 2774.

3. He Wang, Song Zhang, Shili Zhao, Qi Wang, Daoliang Li, Ran Zhao (2022), Real-time detection and tracking of fish abnormal behavior based on improved YOLOV5 and SiamRPN++.

4. Shanhong Zhang et al., 2022. Numerical investigations on temperature and flow field performance of octagonal culture tank under different physical parameters for fish growth based on computational fluid dynamics. Computers and Electronics in Agriculture

5. Shili Zhao et al., 2022. A lightweight dead fish detection method based on deformable convolution and YOLOV4. Computers and Electronics in Agriculture

6. Yu Guo et al., 2022. Dual memory scale network for multi-step time series forecasting in thermal environment of aquaculture facility: A case study of recirculating aquaculture water temperature. Expert Systems with Applications (SCI)

7. Qi Wang et al., 2022. Recent advances of machine vision technology in fish classification. ICES Journal of marine science

8. Mulan Mu et al., 2022. Phase change materials applied in agricultural greenhouses. Journal of Energy Storage

9. He Wang et al., 2022. Fast detection of cannibalism behavior of juvenile fish based on deep learning. Computers and Electronics in Agriculture

10. Shili Zhao, et. al., 2021, Application of machine learning in intelligent fish aquaculture: A review, Aquaculture, Volume 540,

11. Ran Zhao et al., 2022. Formation control of multiple underwater robots based on ADMM distributed model predictive control. Ocean Engineering